Campaign Monitor to Panoply

This page provides you with instructions on how to extract data from Campaign Monitor and load it into Panoply. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Campaign Monitor?

Campaign Monitor is a SaaS email marketing platform that enables businesses to create, send, and manage branded email. The product includes a drag-and-drop builder, visual journey designer, and real-time performance metrics. It offers list management tools to help organizations build segmented lists based on their own criteria, and optimization tools to help users track the effectiveness of their campaigns.

What is Panoply?

Panoply is a fully managed data warehouse service that can spin up an Amazon Redshift instance in just a few clicks. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration. With Panoply, you can use your favorite analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

Getting data out of Campaign Monitor

You can use Campaign Monitor's RESTful API to get data about clients, campaigns, lists, and more into your data warehouse. For example, to get a campaign summary, you could GET GET /campaigns/{campaignid}/summary.{xml|json}. The final parameter specifies whether the data is to be returned in XML or JSON format.

Sample Campaign Monitor data

Here's an example of the kind of response you might see when querying a campaign summary and specifying that the data returned should be in JSON format.

Loading data into Panoply

Once you have identified all of the columns you want to insert, you can use the CREATE TABLE statement in Panoply's Redshift data warehouse to create a table to receive all of the data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Keeping Campaign Monitor data up to date

Now what? You've built a script that pulls data from Campaign Monitor and loads it into your data warehouse, but what happens tomorrow when you have new campaigns?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Campaign Monitor's API results include fields like Date that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Other data warehouse options

Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, and To S3.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to move data from Campaign Monitor to Panoply automatically. With just a few clicks, Stitch starts extracting your Campaign Monitor data via the API, structuring it in a way that's optimized for analysis, and inserting that data into your Panoply data warehouse.